Department of Medical Statistics, Sun Yat-Sen University, Guangzhou, China.
Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
JMIR Public Health Surveill. 2022 Mar 25;8(3):e25658. doi: 10.2196/25658.
Identifying the key factors of Guillain-Barré syndrome (GBS) and predicting its occurrence are vital for improving the prognosis of patients with GBS. However, there are scarcely any publications on a forewarning model of GBS. A Bayesian network (BN) model, which is known to be an accurate, interpretable, and interaction-sensitive graph model in many similar domains, is worth trying in GBS risk prediction.
The aim of this study is to determine the most significant factors of GBS and further develop and validate a BN model for predicting GBS risk.
Large-scale influenza vaccine postmarketing surveillance data, including 79,165 US (obtained from the Vaccine Adverse Event Reporting System between 1990 and 2017) and 12,495 European (obtained from the EudraVigilance system between 2003 and 2016) adverse events (AEs) reports, were extracted for model development and validation. GBS, age, gender, and the top 50 prevalent AEs were included for initial BN construction using the R package bnlearn.
Age, gender, and 10 AEs were identified as the most significant factors of GBS. The posttest probability of GBS suggested that male vaccinees aged 50-64 years and without erythema should be on the alert or be warned by clinicians about an increased risk of GBS, especially when they also experience symptoms of asthenia, hypesthesia, muscular weakness, or paresthesia. The established BN model achieved an area under the receiver operating characteristic curve of 0.866 (95% CI 0.865-0.867), sensitivity of 0.752 (95% CI 0.749-0.756), specificity of 0.882 (95% CI 0.879-0.885), and accuracy of 0.882 (95% CI 0.879-0.884) for predicting GBS risk during the internal validation and obtained values of 0.829, 0.673, 0.854, and 0.843 for area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, respectively, during the external validation.
The findings of this study illustrated that a BN model can effectively identify the most significant factors of GBS, improve understanding of the complex interactions among different postvaccination symptoms through its graphical representation, and accurately predict the risk of GBS. The established BN model could further assist clinical decision-making by providing an estimated risk of GBS for a specific vaccinee or be developed into an open-access platform for vaccinees' self-monitoring.
确定格林-巴利综合征（GBS）的关键因素并预测其发生对于改善 GBS 患者的预后至关重要。然而，目前几乎没有关于 GBS 预警模型的出版物。贝叶斯网络（BN）模型在许多类似领域中是一种准确、可解释且对交互敏感的图形模型，因此值得尝试用于 GBS 风险预测。
本研究旨在确定 GBS 的最重要因素，并进一步开发和验证用于预测 GBS 风险的 BN 模型。
从美国（1990 年至 2017 年从疫苗不良事件报告系统中获得）和欧洲（2003 年至 2016 年从 EudraVigilance 系统中获得）的大规模流感疫苗上市后监测数据中提取了 79165 例和 12495 例不良事件（AE）报告，用于模型开发和验证。使用 R 包 bnlearn 构建初始 BN 时，纳入 GBS、年龄、性别和前 50 种常见 AE。
年龄、性别和 10 种 AE 被确定为 GBS 的最重要因素。GBS 的后验概率表明，50-64 岁的男性疫苗接种者且无红斑应引起临床医生警惕或告知其 GBS 风险增加，尤其是当他们还出现乏力、感觉减退、肌肉无力或感觉异常等症状时。建立的 BN 模型在内部验证中的受试者工作特征曲线下面积为 0.866（95%CI 0.865-0.867），灵敏度为 0.752（95%CI 0.749-0.756），特异性为 0.882（95%CI 0.879-0.885），准确性为 0.882（95%CI 0.879-0.884），用于预测 GBS 风险，在外部验证中分别获得 0.829、0.673、0.854 和 0.843 的受试者工作特征曲线下面积、灵敏度、特异性和准确性值。
本研究结果表明，BN 模型可有效识别 GBS 的最重要因素，通过图形表示来提高对不同疫苗接种后症状之间复杂相互作用的理解，并准确预测 GBS 风险。建立的 BN 模型可以通过为特定疫苗接种者提供 GBS 风险的估计值，或开发为疫苗接种者自我监测的开放访问平台，进一步协助临床决策。
